1. Field
One or more exemplary embodiments of the present disclosure relate to a noise-removing method and system, and more particularly, to a noise-removing method and system for removing a noise element from an image by analyzing an image that is obtained by opening or closing a lens iris.
2. Description of the Related Art
An image monitoring system is a system in which cameras are installed in several locations that need monitoring, so as to perform real-time monitoring and to enable a follow-up search by transmitting images, obtained from the cameras, to a monitor or storing the images in a storage apparatus. In most cases, a monitoring camera, mainly used for an image monitoring system, needs to operate for 24 hours. Accordingly, an image of a certain degree of quality may be generated in a low-illumination environment, such as in a dark location or at night. However, it is highly possible that noise occurs in an image in a low-illumination environment.
Accordingly, techniques for obtaining a monitoring image in which noise is reduced in a low-illumination environment are being developed. Representative methods may include a three-dimensional (3D) filtering method using a structure tensor of an image and a non-local mean-based method.
The 3D filtering method using a structure tensor is a method including the operations of calculating a gradient between neighboring pixels for each pixel of an image, generating a structure tensor based on the gradient, and calculating an eigenvector and an eigenvalue based on the generated structure tensor. Then, based on this calculation, a covariance matrix, a scaling matrix, and a rotation matrix of a 3D Gaussian distribution are calculated, and ultimately, a 3D Gaussian filtering kernel optimized for a corresponding pixel is generated. However, with regard to the 3D filtering method, since a great amount of calculations are required for generating and employing a kernel and noise is not steadily removed, it may be difficult to manufacture a product which uses this method.
The non-local mean-based method employs the concept that a feature in a local area of an image is also found in another area of the image. The non-local mean-based method includes the operations of, with regard to a block of a specific size, searching for blocks similar to the block of a specific size, gathering found blocks, performing noise-removing filtering appropriate for the feature on the found blocks, and then, locating the filtered blocks back to their original location. The non-local mean-based method provides high performance of removing image noise. However, blocks similar to a specific block need to be searched for in an entire image, and a block artifact may occur.